Over the last several years, there has been great interest in the use of the Expectation-Maximization (EM) algorithm for maximum likelihood (ML) reconstruction in emission tomography. We have demonstrated that by including the resolution limitations of the detection system in the model, ML reconstruction can remove the partial volume effect found in filtered backprojection (FBP) reconstructions. This analysis was performed with a realistic brain slice simulation, anatomically defined ROIs, and a complete physical model of our scanner. The price of this removal of bias is an increase in noise and increased computational burden. The next phase of this study is to determine whether ML will provide an important advantage in addressing biological questions with PET by generating unbiased concentration estimates. An important area of interest here is the application of tracer kinetic models to PET where data without the partial volume effect may provide a unique advantage in model determination and parameter estimation. The continuation of this project will involve 3 components: 1) Application of the current algorithm to human data already collected to determine to what extent accurate measurements of radioactivity concentration after the interpretation of PET results. A primary application will be in the comparison of subjects with or without cerebral atrophy with regions-of-interest determined from registered MRI scans. 2) Application to the measurement of the time-activity curve in the basal ganglia following administration of F-dopa. This study will determine if the model configuration necessary to describe the data is altered by the removal of the "background" activity caused by the partial volume effect. This method may also be applied to other kinetic data including human cyclofoxy studies or upcoming animal raclopride studies. 3) Bayesian extensions to the maximum likelihood algorithm. With registered MRI scans, constraints can be applied to the estimation procedure that can reduce variability and speed convergence.